Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2009-2010

CONTENTS■ Message from Director-General1■ Organization of the Institute2■ Introduction to Our Research Centers

4■ Research Cooperation

International Cooperation

16

Research Collaboration

16■ Graduate School Program17■ Outreach Activities

Tutorial Courses

20

Statistical Mathematics Seminar

21

Open Lecture

21

Consultation about Statistical Science 22
Annual Research Meeting for 200822

■ Finance and Buildings

23■ Facilities and Equipment

26■ Organization27■ History

32

Message from Director-General
Message from Director-General

It has already been six years since the Institute of Statistical Mathematics was incorporated into and became a member of the Research
Organization of Information and Systems. During this period, we haveproceeded with research projects focused on current important subjects,
including the establishment of strategic research centers in 2005 inaddition to the existing departments that conduct basic research. The
Risk Analysis Research Center has since steadily developed, andcurrently, its four research groups are engaged in the fields of food anddrug safety; environmental risks; financial risks and insurance; andreliability and quality assurance of services and products. In particular,
since the center’s NOE-forming project was approved for special funds
for education and research this fiscal year, the center has started moreearnest efforts. The Prediction and Knowledge Discovery ResearchCenter has proceeded with four research projects consisting of researchon statistical seismology, data assimilation, molecular evolution, andstatistical genome diversity. Among other activities, the Data Assimilation Research Group’s activity is being established as a next-generation
simulation method that can be adopted not only in global environmental
prediction but also in other extensive areas. At the Research Innovation
Center, which was established two years ago, four research groups are
performing activities in order to create research areas on statisticalscience. The Advanced Monte Carlo Algorithm Research Group has
employed a foreign professor for the first time and started a top-levelstudy.
The relocation to Tachikawa, which was approved at a Cabinet meeting in 1988, finally realized in October this year. It was also decided toconstruct a guest house required for joint research projects involving
stays, which had been a pending issue, and its construction is about
to start and is expected to be completed within this fiscal year. Aftermoving to Tachikawa, the institute will strive to play the role as aninter-university research institute based on new concepts, such as NOE
formation, in addition to conventional application-based joint research
projects, by making the most of the larger joint research space andthe facilities of the new campus. In line with the remarkable progressof information and communications technologies, both the society
and the academic research circles are about to drastically change.
Noticeably, the use of large-scale data will be the key to the progress ofscience and technology in the future, and it is imperative to establish
data-centric science, which is regarded as a fourth scientific methodology. Statistical research, whose major goal is to perform scientificreasoning based on data, will become even more important in the
future. We look forward to your understanding and support for theactivities of the Institute of Statistical Mathematics.

Genshiro Kitagawa
Director-General
The Institute of Statistical Mathematics

1

Organization of the Institute

Basic Research
Department of Statistical Modeling

Organization of the Institute

The Department of Statistical Modeling conducts research on the modeling of causally, temporally and/or
spatially interrelated complex phenomena, including intelligent information processing systems. It also conducts
on model-based statistical inference methodologies.

䡵 Spatial and Time Series Modeling GroupThe Spatial and Time Series Modeling Group works on modeling and inference for the statistical analysis of time series,
spatial and space-time data, and their applications to prediction and control.

䡵 Intelligent Information Processing GroupThe Intelligent Information Processing Group works on concepts and methods for the extraction, processing and transformation of information in intelligent systems, motivated by an active interest in practical problems in engineering and science.

䡵 Graph Modeling GroupThe Graph Modeling Group works on analyses of the data generated by systems with a graph structure and on the modeling required in order to reconstruct the original system.

Department of Data Science
The Department of Data Science aims to develop research methods for surveys, multidimensional data
analyses, and computational statistics.

䡵 Survey Research GroupThe Survey Research Group focuses on research related to statistical data collection and data analyses.

䡵 Multidimensional Data Analysis Group
The Multidimensional Data Analysis Group studies methods for analyzing phenomena grasped on multidimensional
space and ways for collecting multidimensional data.

䡵 Computational Statistics GroupThe Computational Statistics Group studies sophisticated uses of computers in statistical methodology such as
computer-intensive data analyses, computational scientific methods and statistical systems.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, the theory of optimization, and the practice of statistics in science.

䡵 Mathematical Statistics Group
The Mathematical Statistics Group is concerned with aspects of statistical theory and probability theory that have statistical applications.

䡵 Learning and Inference Group
The Mathematical Statistics Group develops statistical methodologies that enable researchers to learn from data sets andto properly extract information through appropriate inference procedures.

䡵 Computational Mathematics GroupThe Computational Mathematics Group studies computational algorithms together with mathematical methodologies used
for statistical modeling in the sciences.

Strategic ResearchPrediction and Knowledge Discovery Research Center
The Prediction and Knowledge Discovery Research Center studies the statistical modeling and inference algorithms
that can be used to extract useful information from the huge amount of data which complex systems produce, and thus
attempts to solve real-world problems in many different scientific domains, especially genomics, earth and space sciences.

䡵 Molecular Evolution Research Group
The Molecular Evolution Research Group researches the area of molecular phylogenetics, and seeks to develop statisticalmethods for inferring evolutionary trees of life using DNA and protein sequences.

䡵 Data Assimilation Research GroupThe Data Assimilation Research Group aims at developing new, advanced data assimilation techniques to combine
different information from dynamical simulation and observation data.

2䡵 Statistical Seismology Research GroupThe Statistical Seismology Research Group is concerned with the evaluation of seismicity anomalies, detection of crustal
stress changes, their modeling, and the probability forecasting of large aftershocks and earthquakes.

䡵 Statistical Genome Diversity Research GroupThe Statistical Genome Diversity Research Group aims to construct novel methodologies for learning and inference from avariety of data sets in the rapidly growing area of bioinformatics.

Organization of the Institute

Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to the study of the increased uncertainty
and risk associated with the increasing globalization of society and the economy. The center is also constructing a network for risk analysis in order to contribute to the creation of a reliable and safe society.

䡵 Food and Drug Safety Research GroupThe Food and Drug Safety Research Group aims to develop the statistical framework and methodology of quantitativerisk evaluation for substances ingested by the human body.

䡵 Environmental Risk Research GroupThe Environmental Risk Research Group studies the statistical methodologies related to environmental risk and environmental monitoring.

䡵 Financial Risk and Insurance Research GroupThe Financial Risk and Insurance Research Group explores the use of statistical modeling methods to quantify the risks
involved with financial instruments and insurance products.

䡵 Research Group for Reliability and Quality Assurance of Service and Product
The Research Group for Reliability and Quality Assurance of Service and Product aims to increase the safety of products
and services by developing statistical methods that contribute to quality assurance and reliability and by promoting the
adoption of these methods in the industrial world.

Research Innovation Center
The objective of this center is to establish innovative research in statistical mathematics to keep up with new
trends in the academic and real worlds. The center carries out original research projects, ranging over bothpure and applied frontiers.

䡵 Social Survey Information Research GroupThe Social Survey Information Research Group collects several social survey results with a view to assembling them todevelop a statistical methodology to describe the social world.

䡵 Functional Analytic Inference Research Group
This group aims to develop nonparametric methodology for statistical inference using reproducing kernel Hilbert spaces
given by positive definite kernels, and to apply these techniques to causal inference problems.

䡵 Advanced Monte Carlo Algorithm Research GroupThe Advanced Monte Carlo Algorithm Research Group aims to develop Markov Chain Monte Carlo and Sequential Monte
Carlo algorithms and study their applications.

䡵 Random Number Research Group
This group carries out research into random number generation, including hardware random number generators, andtesting randomness with methods such as time series analysis.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists the development of statistical science by managing
the computer systems used for statistical computing, facilitating public outreach, and supporting the research
activities of both staff and collaborators.

䡵 Computing Facilities Unit The Computing Facilities Unit is in charge of managing computer facilities, software forresearch, networking infrastructure and network security.

䡵 Information Resources Unit The Information Resources Unit is responsible for maintaining an extensive library and an electronic
repository, and is in charge of planning statistical education courses to popularize research results.

䡵 Media Development Unit The Media Development Unit is in charge of the publication and editing of research results
and is responsible for public relations.

3Introduction to Our Research Centers
Prediction and Knowledge Discovery Research Center

Molecular Evolution Research Group

Toward Integrative Understanding of Biodiversity

Introduction to Our Research Centers

The aim of this project is to understand thebiodiversity on Earth from various view points
encompassing from molecular level to ecologicallevel. Evolutionary view is indispensable in the
integrative understanding of the biodiversity,and the methods for inferring molecular phylogeny are essentially important. In this project, weare developing models of nucleotide substitutions
during evolution and methods for phylogenetic
inference based on maximum likelihood. In developing the statistical methods, we simultaneously
try to solve real problems of biological importance.
The biological problems we are working on
include evolution of vertebrates such as mammals
and birds, evolution of land plants, and the biodiversity of Malagasy fauna. Madagascar has been
isolated from any continents for a long geologicaltime, and has developed a unique fauna withhigh level of combined species richness andendemism. In Madagascar, we are working on thebiogeographic problems of tenrecs and lemurs(mammals) and baobabs (plants). Spiny tenrec ismorphologically very similar to hedgehog, andhad long been classified in Insectivora together
with hedgehogs, moles, and shrews, but molecular phylogenetic analyses clarified that tenrecs
belong to Afrotheria which includes elephants,
hyraxes, and aardvarks, and that the similarity
between spiny tenrec and hedgehog is due to convergent evolution. Estimation of the time-scale oflemurs and baobabs gave important clues inclarifying the origin of these groups of organisms
in Madagascar.
Jun Adachi

Continental configuration of 100 Myr BP
and Eutherian evolution

Convergent evolution: Hedgehog and Tenrec

Boreotheria
Hedgehog
Eu-insectivora

Tenrec(endemic in Madagascar)

Afrotheria

Afro-insectivora

Xenarthra

Lemurs present remarkable diversity in Madagascer

Phylogenyof Primates

Lemuridae
Prosimianoutside Madagascar

Prosimianin Madagascar

Cheirogaleidae

4

Indriidae

Daubentoniidae

Data Assimilation Research Group

Data Assimilation: Time-dependent Information Fusion from Numerical
Simulation and Large-scale Observation
our environment. We are therefore constructing
new computation methods based on the sequential DA methods and conducting five DA projects.
One is done with the ensemble Kalman filter that
assimilates the TOPEX/Poseidon altimetry to thecoupled ocean-atmosphere simulation model.
The second DA is done with a particle filter for
Tsunami simulation model to correct bottom
topography. The third DA project is aimed at finding an uncertainty in sea bottom topography
which plays an important role in conducting an
ocean tide simulation. The fourth is the DA project
to estimate distributions of ring current ions andelectric potential in the inner magnetosphere by
assimilating the series of the ENA data obtainedby the HENA imager on board of IMAGE satellite
into a kinetic ring current model (CRCM). The last
project is to apply the DA methodology to combine
a simulation model with observed data like microarray gene expression data for understanding biological pathways.
Tomoyuki Higuchi

Introduction to Our Research Centers

Data Assimilation (DA) is a technique for a synthesis of information from a dynamic (numerical)
model and observation data. It is an emerging
area in earth sciences, particularly oceanography,
stimulated by recent improvements in computational and modeling capabilities and the increasein the amount of available observations. In statistical methodology, DA can be formulated in thegeneralized state space model, where the systemand observation model correspond to large-scale
numerical model-based simulations and large-scalesatellite- and/or ground-based measurement systems, respectively. Past studies for DA employed alinear Gaussian state space model and applied
Kalman filter. The Kalman filter based methods,
however, do not allow for the strong nonlinear
and/or non-Gaussian disturbance behaviors. Many
phenomena in earth sciences tend to be discussed
in terms of a complex system in which the nonlinear non-Gaussian fluctuations (disturbances) play
an important role. The nonlinear non-Gaussian
DA method needs to be developed in an attempt
to improve a performance of prediction ability ofPrior distribution of the parameters( Left: 100,000 particles, Right: 100,000,000 particles ).

100,000 particles

100,000,000 particles
Posteriori distribution of parameter.

Figure 1: Numerical experiments of the data assimilation based on the particle
filter technique for developing the personalized simulation tools
for the living matter.

Figure 2:
Data assimilation research is carried out
with a wide variety of the parallel computing systems including the handmade
parallel computing system (above) as
well as a general-purpose super computer.

5

Prediction and Knowledge Discovery Research Center

Statistical Seismology Research Group

Location Dependent Space-time ETAS Model for Wide Regional
and Global Seismicity

Introduction to Our Research Centers

We consider the space-time ETAS (epidemic-typeaftershock sequence) model which takes accountof different regional physical characteristics ofthe earth crusts. Its parameters such as p-value foraftershock decay vary from place to place. The
parameter variations of the estimated space-timemodel are visualized and used to investigate features of the regional seismic activity.
Specifically, each parameter is a 2-dimensional
piecewise linear function whose value at a location is linearly interpolated by the three values atthe location of the nearest three earthquakes(Delaunay triangle vertices) on the tessellated
plane by epicenters. Such modeling by using Delaunay tessellation is suited for the observation onclustered points. The estimates of the parameter
functions are simultaneously adjusted by thepenalized log-likelihood that defines a trade-off
between the goodness of fit to the data and unifor-mity constraint of the function (i.e., each facet ofthe piecewise linear function being as flat as possible). The constraint can be objectively adjusted
from the data by means of an empirical Bayesian
method using the Akaike’s Bayesian information
criterion (ABIC)We are particularly concerned with the spatial
estimates of the first two parameters of thespace-time model: namely, μ-values of the background seismicity for the long-term prediction of
the large earthquakes; and aftershock productivity K-values for the immediate aftershock probability forecast. The reasons and their utility of themodel have been demonstrated by applying it toJapan and global seismicity. For both objectives,
we further need the spatial estimates of the
Gutenberg-Richter b-values that are also modeledusing similar function on Delaunay tessellated
space and optimized by the ABIC procedure.
Yosihiko Ogata
0.1

35

M>=5
M>=5,
=5,, 1926-2005
1926-200
1926
2005

35

0 –2

Background
Backgroun
Background M5+
Seismicity/da(events(ev nts / deg2 /day)

30

30

25

25

10 –3

20

20

10 –4
10 –5

130

135

140

145

130

135

140

145

10 –6
35

35

30

Aftershock M5+
productivit
productivity(ev nts / deg2 /day)/da(events

10 –7
30

1.1

ues ofvalue
p - values
aftershock decay

1.05

25

25

10 –8

20
130

6

1.2
1.15

135

140

145

0.95
0.9
0.8

20
130

135

140

145

Introduction to Our Research Centers

Statistical Genome Diversity Research Group

Gene Selection for Association Study from Gene Expressions
to Phenotypes

䡵 Gene selection
We presently analyze gene expressions data andclinical data with treatment effect by joint workwith a research group in National Cancer Center.
The objective is to exploit a prediction method fortreatment effect given a set of gene expressions by
microarray. For this we build the following threesteps:
1. Around 26,000 gene expressions are filtered toaround 2,500 gene expressions by referring toknowledge of gene function discussed in theliterature.
2. Pick up the best 100 gene expressions according to the absolute value of 2,500 correlation
coefficients with the binary outcome forprognosis.
3. AdaBoost for pattern recognition is sequentially applied to the data sets from the best 5
gene expressions by variable increment
method.
Figure 1 is a hierarchical clustering plot in 100
gene expressions and the binary outcome. Thisshows possibly a strong association between gene
expression and treatment effect. Figure 2 is theresult of AdaBoost for pattern recognition from 5
gene expressions to the outcome based on 25
training samples and 16 test samples. We result
good performance with 15 hit out of 16 samples. In
future we plan to get a new data set to validate thisresult in several aspects for clinical information.
Shinto Eguchi

Traning data set
Error rate: 0.0%
Figure 1: Hierarchical clustering for 100 genes and 25 subjects.

Introduction to Our Research Centers

䡵 Gene expression association study
We aim at proposing statistical methodology toprovide efficiently knowledge discovery on thebasis of genomics and omics data sets that are
produced by tremendous developments in bioscience and biotechnology. In gene expression association studies methods for data analyses are quite
contrast between research fields of bioinformatics
and medicine. This comes from how does one
trade off double-edged errors of false discovery
and missing discovery in the presence of typicalaspects in high-dimensional data and small
sample size. However that two different methods
are existing is problematic.
Nevertheless such controversy one strongly
expects realization for personalized treatments
based on genome and omics information. In fact,
the prognosis prediction kit, called MammaPrint
was accepted by Food and Drug Administrationin U.S. In this way there is left the basic problems
unsolved as mentioned as above. Our researchgoal is to build a consistent method to analyze
genome data by fusion between the methods inclinical medicine and bioinformatics.

Test data set
Error rate: 6.25%

Figure 2: 5 probe classification by AdaBoost

7

Risk Analysis Research Center

Food and Drug Safety Research Group

Benefit-risk Balance Evaluation of Food and Drug
䡵 Mission
Social responsibility of the food and drug safety
program in RARC is to design appropriate databases
and statistical methods for the benefit-risk balanceevaluation of food and drug.

Introduction to Our Research Centers

䡵 Benefit/risk analysis of drugs basedon large-scale database
Various large-scale databases have been available
for the scientific benefit / risk evaluation and risk
management of post-marketing drugs in Europe
and North America. In Japan, however, such alarge-scale database does not exist. As the first stepto improve this situation, we are building original
databases using data collected from pre-marketingclinical trials and post-marketing surveillance,
and we conduct benefit / risk analyses of drug effect.
䡵 Genome-wide association study between
phenotype and large-scale SNPs data
We study the relationship between effectivenessof treatment for hypertension and about 500,000
SNPs genotype data to find candidate genes forthe good selection of drug for hypertension. Aftera separation of training group or test group fromthe sample, we examine a genome-wide association
test for training group. Then it is gotten Figure 1
shows the loci and LD (D' ) map with chromosome
position and LD blocks on chromosome 2. LD maps
have been made using Haploview (Barrett et al.,2005) that is a very popular program in the statistical genetic world. For each LD block, we estimate
a penetrance and test the likelihood ratio for SNPs
and haplotypes to obtain candidate genes. If resultsof candidate genes are reproduced in the test
group, it may be useful information for good selection of antihypertensive drug.
䡵 Ad hoc study on a specific drug safety issue
An issue of drug safety often becomes a social
concern, and the immediate scientific elucidationis required. An appropriate study design, implementation and statistical analysis are necessary
to get an accurate scientific quantitative answer.
We participate in an ad hoc study on a specificdrug safety issue as experts of statistical scienceand epidemiology.
Toshiharu Fujita

Figure 1: Ldmap(D’) and LD blocks on Chr.2 by Haploview

8Introduction to Our Research Centers

Environmental Risk Research GroupThe Effort of the Statistical Science towards Solutionof Environmental Problems
quality data of Tokyo Bay and is analyzing thesedata to reveal long-term variation of water quality.
(photo 1, photo 2)
䡵 On persistent organic pollutants
To identify the sources of pollution by persistentorganic pollutants (POPs), this study is developing
statistical methods to make inference on pollution
sources besides organizing POPs data. Further,
this study is making contribution to the development of technology to reduce the risk of POPs invegetable farming. (photo 3, photo 4)

Introduction to Our Research Centers䡵 Mission
A risk analysis research center / environmental
risk research section aims at performing the contribution towards solution of the environmental
problem which is a modern subject by developing
the optimal new statistical methodology for eachenvironmental problem (for example, the problem
of dioxin, the problem of global warming, theproblem of continuous use of safe water). Moreover, in order to realize this purpose, in cooperation with the community of environmental science,research is carried out including a visiting teacher
or a project researcher.

䡵 Risk-hedge oriented optimization systemagainst natural and managerial disaster toward䡵 Long-term variation of water quality in Tokyo Bay
sustainable forest resource management
Although the pollution load flowing into Tokyo
After the Kyoto protocol entered into force,
Bay has been reduced, red tide still breaks out
reduction of carbon dioxide in the atmosphere isfrequently. Red tide increases the amount of oxygen
promised to slow down the degree of global warmdeficient water mass, and this sometimes causes
ing. Among the proposed mechanisms under theblue tide. The occurrence of red and blue tides
Kyoto protocol, forest resources are regarded as
seems to be connected with elevated water temperaa source of carbon sequestration, where forestature and change in salinity of recent date in Tokyotion, deforestation and reforestation are the main
Bay. This study is making a collection of water
human-induced activities. Housing and furniture manufacturing is also contributing tocarbon mitigation indirectly. Despite of thesefacts, unfavorable current economic situation
for forestry business discourages forest owners
to abandon forestry practice in mountainous
regions. Besides, typhoon disaster and snow
damage on forest stands is accelerating abanPhoto 1: Red tide (The Tokyo Metropolitan
Photo 2: Blue tide
doning phenomena. In our research project,
Research Institute for Environmental Protection) (Chiba Prefectural Environmental Research Center)we construct a risk-hedge oriented optimization system toward sustainable forest resource
management. In the system, we try to predict
events of natural disaster and managerial disaster spatially and temporally, and control the
management scheme to avoid these disasters
with a view of these phenomena. (photo 5, Fig 1,
Photo 3: Growing experiment of cucumbers Photo 4: Measurement of POPsFig 2 )
(Niigata Agricultural Research Institute)(National Institute for Agro-Environmental Sciences)
Koji Kanefuji
Wait

Harvest-Plant

Harvest-Abnd

Photo 5: Windstorm in Slovakia

Figure 1: Fire Management

Figure 2: Decision Tree

9

Risk Analysis Research Center

Financial Risk and Insurance Research Group

Additional Loan and Credit Risk

Introduction to Our Research Centers

䡵 Aim of our group
Financial Risk and Insurance Research Group
mainly concerns the theory and practice in thequantitative analysis of risks involved with various
financial instruments and insurance products fromthe view point of statistical modeling. Various
research projects are under way not only by ISM
researchers but by visiting and post doctoral
researchers. This article introduces a cooperative
research by Dr Satoshi Yamashita (Associate Professor) and Mr Toshinao Yoshiba (Visiting Associate
Professor, Bank of Japan).

loan so that it may decrease the expected loss ofthe bank. In that sense, supplying the additionalloan is a rational behavior of the bank.
䡵 Expected Loss of the Bank under Downturn
Macroeconomic Condition
Even though the additional loan is rational, if
the firm’s asset movement is highly correlatedwith the macroeconomic condition, the loss ofthe bank may increase when the macroeconomic
condition worsens. Under the major premise thatthe bank supplies an additional loan to minimize
the expected loss at some time, this researchanalytically derives the conditional expected loss
with some stressed condition when the macroeconomy is in recession. Figure 2 depicts how theconditional expected loss varies with respect tothe correlation between the firm’s asset movement and the macroeconomic condition. Whenthe correlation is large, so is the conditional
expected loss, which implies banks should holdlarge capital in recession.
Yoshinori Kawasaki

䡵 Additional Loan and Loss of the Bank
Credit risk models investigate the default probability of a firm and the recovery rate of the loan
to the firm. Popular one-period structural models
capture the credit risk by the relationship between
asset and liability of the firm. And they assume
that the loan amount does not change until thematurity. However, a bank sometimes supplies anadditional loan to the firm, in practice.
Even if expected growth rate of the firm is high,the firm’s asset may become worse. In Figure
1, time t in the horizontal axis is the case. If
nothing is done, further asset deterioration
may lead to the bankruptcy in such a casemost of the account cannot be collectible forthe bank. On the other hand, the bank canstimulate the firm by supplying an additional

Calculate EL
Distributionof AT

Liability after
adding loan D+ Δ

Loss LT
Asset value after
adding loan At +Δ

Assetvalue A0

dAs = µ As ds + σ As dWs

Liability D

Additionalloan amountΔ

Coveredportion

T

time

At

0

t

Figure 1: Schematic diagram of the model

Conditional
Expected Loss

Current
Asset Value A 0
Figure 2: Correlation and Conditional Expected Loss

10

Correlation

Introduction to Our Research Centers

Research Group for Reliability and Quality Assurance of Service and ProductConstruction of Statistical Methodologies in Quality Engineering䡵 Outline of research

䡵 SQC and Taguchi method
In SQC, based on experimental data on the quality
characteristics of materials, mechanical equipment,
products and the like, techniques such as the 7 QC
tools, Fisher’s design of experiments method andregression analysis are used to ascertain the current
situation, investigate the causes and analyze theimportant factors. On the other hand, Taguchi method
treat the causes of variation in the customer usage
conditions, environmental conditions and the like asnoise factors, and aim to discover the conditions thatmake the effects of these factors as small as possible.
Visualization ·Absolute evaluation(III) Mechanism
Analysis

Statistical
Science( I ) Variation Factor
Analysis(II) Control of
the Principal
Design Factors

Simplified model ·SurveyCurve fitting ·Relative evaluation

N7

SQC

CAE : approximate solution

MA

DOE

Statistical
Science
Methods

Structure model

Figure 1: Highly Reliable CAE Analysis System Approach Method

In parameter design, inevitable errors resulting fromnoise factors are normally larger than the random
errors dealt with by SQC, so more effort is put into
the descriptive statistics approach rather than into
postulating probability distributions. However, it ispossible to take a statistical approach by introducing
error factors into the model as parameter factors,
and considering the probability distribution of SN
ratios as a statistical quantity, thereby enabling a
discussion of statistical inference.

䡵 Highly Reliable CAE Analysis System
Approach Method

Introduction to Our Research Centers

Quality engineering (Taguchi methods) is a technicalmethodology devised more or less independently by
Genichi Taguchi over half a century for the purposeof making systematic improvements to quality. A key
technique of Taguchi methods is parameter design,
which is known to reduce the “variability” of characteristics at the product design and development stages,
and there have been many reports of successes inmanufacturing industries such as the automotive andelectrical industries as part of the establishment of
Quality-Japan.
But although the development of theoretical methodologies for parameter design in Taguchi methods
is being actively pursued overseas as an extension of
statistical quality control (SQC) or statistical science,in Japan it has only been researched to a limited
extent. Therefore this study aims to devise a statisticalmethodology for quality engineering by clarifying thepoints of similarity between the design of experiments
according to Fisher and Taguchi.

In this study, consideration is given to the validityof “statistical science”, which is necessary for innovation of the development designing process, for use in
a new issue involving numerical simulation technology(CAE) in development designing. This new applicationof statistical science is designed to aid the transition
from the “prototype / experimental based real machine
evaluation method” to the “prediction evaluation
method based on highly reliable CAE analysis.” Morespecifically, concrete examples of “statistical sciencestudy cases that contributed to CAE analysis” fromautomotive manufacturers are examined from thestandpoint of “statistical science contributing to design
science”. This was done so as to propose the “highly
reliable CAE analysis system approach method” asshown in Fig. 1 that contributes to the process innovation of development designing.

䡵 Data Mining for Hospital Management
Organizations in our modern society grow larger
and more complex to provide advanced services dueto the varieties of social demands. Such organizations are highly efficient for routine work
processes but known to be not robust to
unexpected situations. According to this observation, the importance of the organizational risk
management has been noticed in recent years.
On the other hand, a large amount of data on thework processes has been automatically stored
since information technology was introduced tothe organizations. Thus, it has been expectedthat reuse of collected data should contribute torisk management for large-scale organizations. In
this research, we focus on hospital information
system, to which we applied temporal data mining
and exploratory data analysis techniques andsuccessfully quantified several risk factors for
hospital management.
Toshihiko Kawamura
11Research Innovation Center

Social Survey Information Research Group

Building a Database of Social Survey Results

Introduction to Our Research Centers䡵 Objective of the Social Survey Information
Research GroupThe Social Survey Information Research Group of
the Research Innovation Center has been collecting social survey results with the aim of building
a database of social surveys. A single survey
provides limited information, particularly in the
recent deteriorated survey environment. Multidimensional analysis using various types of social
surveys is essential for comprehending the complicated aspects of modern society. This researchgroup studies the methodologies for building a
database and develops statistical methods foranalyzing data. The group also intends to makethe database available for public use.
䡵 A comprehensive report of a survey
on the Japanese national character
The core data used to develop the database is
obtained from a survey on the Japanese national
character, which is conducted on a regular basisby the Institute of Statistical Mathematics. Thissurvey has been conducted every five years since

Figure : Results of a survey on the Japanese national character12

the first 1953 survey. The most recent was its
twelfth survey and was conducted in the autumn
of 2008. The questionnaire items in the survey
concerned the attitudes of the participants toward
life, environment, religion, politics, race, the Japanese people, and so forth. The results of this longitudinal survey, which are crucial for evincing thetransition of Japanese character, are alreadyavailable on the Internet. More comprehensive
results, including cross-tabulation tables andgraphs, will be also provided on the Internet.
䡵 Database of related surveys
The survey on the Japanese national characteris by no means the only survey conducted by theInstitute. Various interrelated comparative surveys
and experimental surveys have also been
conducted. The results of these surveys will also
be included in the database and made accessible
to the public one at a time. The potential usersof this database, including not only researchers
but also the general public, will be able toanalyze the data from various perspectives.
Takahiro Tsuchiya

Introduction to Our Research Centers

Functional Analytic Inference Research Group

Kernel Method: Nonlinear Data Analysis with Efficient Computation

䡵 Dependence analysis with kernels
The kernel method was first developed as“kernelization” of existing linear methods: support
vector machine and kernel principal component

analysis are among such examples. More recent
studies, however, have revealed that it provides amethod for analyzing homogeneity, independence
and conditional independence of variables byrepresenting high order moments with featuremaps. Our group has been making essential
contributions in this line of research, in particularin the characterization of conditional independence
and suitable kernels for such analysis. In addition,
we have proposed a new kernel method of
estimating causal networks from data, which isconstructed based on the kernel device of relevant
conditional independence tests. We also carry out
a project on approximation of large matrix computation necessary for kernel methods.

Introduction to Our Research Centers䡵 What is kernel method ?
Kernel method is a recent methodology for
analyzing data of complex structure. Instead of
pursuing elaborate modeling of data, the method
uses a feature map of data into a high-dimensional
functional space to extract the high order momentsof the data. While methods of mapping data by
power series expansion or basis function expansion have been long used for the high ordermoments, they have a problem of computational
explosion for high-dimension data. The kernel
methods avoid this problem by using the cleverdevice of “positive definite kernel”, which computes
the inner product of the high dimensional
functional space by simply evaluating the kernel
values. This gives an efficient method for incorporating high order moments of data. In addition,
another strong point of the kernel method is thatit provides a seamless methodology for analysis
of non-vectorial data such as network data andsymbol sequence such as texts.

䡵 International research network
Our group has active collaboration networks
with researchers in the world, in particular, with
top researchers in University of California, Berkeley, and Max Planck Institute in Germany. Our
research results have been presented in tutorialsof various international conferences such as
Machine Learning Summer School 2007 (Germany)and International Conference on Machine Learning
2008 (Finland).
Kenji Fukumizu

H
Φ

xi

Feature map

xｊ
Data space

Feature space(functional space)

Figure 1: Feature map of kernel method

Sex

IncomeAgeFinancial
Area

Figure 2: An example of causal network
given by the kernel method

Outlook

Political

13

Research Innovation Center

Advanced Monte Carlo Algorithm Research Group

Rare Event Sampling by Markov Chain Monte CarloIntroduction to Our Research Centers

Our research focuses on the development ofnovel and nonstandard applications of Markov
Chain Monte Carlo algorithm (MCMC). MCMC isintroduced in physics in 1950s by Metropolis andcoworkers and proved to be a useful tool in thefield of Bayesian modeling in 1990s. MCMC is,
however, a general strategy for sampling multivariate distributions with unknown normalization
constants and in principle can be applied to manyother problems.
Here we discuss rare event sampling (i.e.,sampling from the tails of the given distribution)
problem as an example of the promising application fields of MCMC. By using MCMC, especiallywith its variants “Replica Exchange Monte Carlo”
and “Multicanonical Sampling”, we can generatesamples of very small probability, say, 10-15, oreven 10-30, under the given model, and calculate
its probability correctly. Today’s most powerful

Figure 1: Markov Chain Monte Carlo

Figure 2: Rare events at the tail of the distribution

14

computer can never achieve such an ultimate
precision with naïve random sampling.
With this approach we can solve various interesting problems. For example, we can sample veryrare errors in an information processing systems
and access its probability. Also, we can find atypical but important regular orbits in chaotic systems.
For network design problems, MCMC can generate
not only a network that optimizes the given criterion, but also a collection of networks that belong
to the tail of the distribution defined by the given
criterion.
Because these problems are highly crossdisciplinary, we are collaborating specialists ofeach field and exploring frontiers of MCMC with
scientists at Univ. Tokyo, Hokkaido Univ., Osaka
Univ., and Kyoto Univ..
Yukito Iba

Introduction to Our Research Centers

Random Number Research Group

Parallel Pseudo Random Number Generation andHardware Random Number Generator䡵 Random number
Random number is defined a sequence of randomvariables. We think binary random variables withequal probability. It is easy to understand that we
can generate this random numbers form a resultof finite number of honest coin tossing.

Introduction to Our Research Centers䡵 Random number generator
We call methods to generate random numbers
random number generators. The random number
sequence generated by a computer is called pseudo
random numbers. On the other hand the random
number sequence generated by a physical phenomenon is called physical random numbers. In general
pseudo random numbers has uniform distribution
on [0, 1). There are linear congruential method,M-sequence, Mersenne Twister and so on. The
most popular phenomenon which is used for a
hardware random number generator is thermal
noise of electronic circuit. Recently chaos phenomenon of semiconductor laser is used for a hardware
random number generator.

䡵 Parallel pseudo random number generation
Random numbers are used for Monte Carlo
method, MCMC, bootstrap and so on. Large-scalecomputations are necessary to large-scale problems. Large numbers of random numbers arenecessary to large-scale computations. It seems
that we cannot do this computation by only one
CPU. Parallel computations can bring us someresults. Well-known methods for parallel pseudo
random number generation are leap frog method
and cycle splitting method. We cannot use thesemethods free from care, since it is too difficult toconfirm whether two sequences have correlation
or not.
䡵 Hardware random number generator
At the Institute of Statistical Mathematics, hardware random number generators are researched
for a long period. Our group also research new
hardware random number generators which are
faster and have better quality. We show a test
board of new generator. It is important to use
two noise sources. We have plan to product anddeliver new hardware random number generator.
Yoshiyasu Tamura

Figure : Prototype of hardware random number generator

Power
SupplyConnector

RNDSCLK
Terminal

RNDS
Terminal

RNDP Terminal

Pin Assign for RNDP Terminal

15

Research Cooperation

International Cooperation
䡵 Associated Foreign Research Institutes
Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA (Washington)

July 27, 1988 -Stichting Mathematisch Centrum

The Kingdom of the Netherland (Amsterdam)

May 10, 1989 -Statistical Research Center for Complex Systems, Seoul National University

The Republic of Korea (Seoul)

October 17, 2002 -Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany (Berlin)

December 8, 2004 -Institute of Statistical Science, Academia Sinica

Taiwan (Taipei)

June 30, 2005 -The Steklov Mathematical InstituteRussia (Moscow)

August 9, 2005 -Central South University

China (Changsha)

November 18, 2005 -Soongsil University

The Republic of Korea (Seoul)

April 27, 2006 -Department of Statistics, University of Warwick

The United Kingdom (Coventry)

January 16, 2007 -The Indian Statistical Institute

India (Kolkata)

October 11, 2007 -Research Collaboration
This academic study program provides researchers from other academic institutes with access to thefacilities of the Institute, and provides opportunities for researchers to conduct theoretical and appliedstudies on statistics.

Research Cooperation

䡵 Number of Activities
2003

2004

2005

2006

2007

2008

99

108

124

122

120

138

䡵 Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below tofind the most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and time series modelingfComputational statisticsbIntelligent information processinggMathematical statisticsc

Graph modelinghLearning and inferencedSurvey researchiComputational mathematicseMultidimensional data analysisj

OthersMajor Research Fields
Number16

FieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, low, politics, society, management, official statistics, population, etc.

8

OthersOther research fields

Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of the Graduate University for Advanced
Studies (the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate
education. Since its opening, the Institute has created the Department of Statistical Science and, since April
1989, has accepted students for education and research in doctoral programs. In 2006, the Institute adopted
a five-year system, offering either a five-year education and research program, or a three-year education andresearch program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Cyber Society and Culture
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science
Accelerator Science

President
School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics
Basic Biology

School of Life Science

Physiological Sciences
Biosystems Science

School of Advanced Sciences

Photo Science

Graduate School Program

Evolutionary Studies of Biosystems

Outline of Education and Research
The course includes modeling, forecasting, inference and designing of data-gathering systems in order
to extract information and knowledge from the real world based on empirical data, as well as basic, mathematical and applied education and research related to these fields. The course aims to provide thestudent with skills that help to contribute to solving important and connected issues and give the ability
to perform original research.
Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information processing.
We also provide education and research on statistical inference based on various models, methods of calculation
for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

17

Features of Education and Research The course is the only integrated doctoral program on statistical science in Japan. It has received
students from a wide variety of disciplines and has offered education and research on statisticalscience by professors specialized in many different fields, from theory through to practical applications.
 The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, aswell as a variety of software, including original statistical software developed by the Institute.
 The academic publications and books on statistical and mathematical sciences produced are some of
the best in the world.
 The library holds an extensive collection of books and journals, being one of the best in the world.
 In its role as an inter-university research institute, the Institute holds frequent workshops and seminars
by visiting professors and researchers from both Japan and abroad. Students are free to attend andparticipate.
 It is possible to collaborate with researchers from other universities and institutions. It is also possible
for students to develop their own projects by participating in research projects with other institutions
through the Transdisciplinary Research Integration Center, Research Organization of Information and
Systems.

Course Requirements and Type of Degree Granted
 Requirements to complete the course are as follows:
Completion of at least 40 credits while a graduate student of five years, or completion of at least 10
credits while a doctorate student of three years who graduated Master’s course meeting all the criteria
set by the thesis committee of the Institute and successfully completing the final examination.
 On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly withan inter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.
 The required number of years of study will be flexible if a student demonstrates outstanding research
results.

Graduate School Program

Number of Students ( As of April 1, 2009 ) Doctor’s course three years
Year of enrollment

2002

2003

2004

2005

2006

2007

2008

2009

Number of students

1

1

3

2

1 (1)

5

6

4

* The figures in parentheses indicate the number of foreign students being supported at government expense.
* The figures in circles indicate those who are employed by other organizations.

 Doctor’s course five years

18

Year of enrollment

2006

2007

2008

2009

Number of students

1

2

2



Graduate School Program

University Background of Students
National and public universities• Hokkaido University • Tohoku University (2) • Fukushima University • University of Tsukuba (5) • Saitama University• Ochanomizu University • Hitotsubashi University (5) • Chiba University • The University of Tokyo (14) • Tokyo Gakugei University• Tokyo University of Agriculture and Technology • Tokyo Institute of Technology (2) • Tokyo University of Marine Science and Technology• Nagoya University (2) • Toyohashi University of Technology • Kyoto University (3) • Osaka University • Okayama University• Shimane University (2) • Kyushu University (2) • Oita University • Japan Advanced Institute of Science and Technology, Hokuriku• Osaka City University
Private universities• Keio University (4) • Chuo University (6) • Tokyo University of Science (5) • Toyo University • Nihon University (2)• Japan Women’s University • Hosei University (7) • Waseda University (5) • Nanzan University (1) • Kyoto Sangyo University• Okayama University of Science • Kurume University
Foreign universities• Aston University • Center for Analysis and Prediction, China Seismological Bureau• Chinese Academy of Sciences, Institute of Applied Mathematics • Jahangirnagar University (2) • Northeastern University, China• Ohio University • Stanford University • The Hong Kong University of Science and Technology • Universidade Eatadual of Campians• University of Colorado at Boulder (2) • University of Dhaka (2) • University of Hawaii • University of Malaya • University of Rahshahi• University of Science and Technology of China

Degrees AwardedYear

Doctor of Philosophy

Year

Doctor of Philosophy

1996
1997
1998
1999
2000
2001
2002

3 [1]
1
4 [1]
6
5
5
4

2003
2004
2005
2006
2007
2008

8 [1]
4
4
8 [1]
7 [1]
4 [1]

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

Current Position of Alumni ( As of April 1, 2009 )Graduate School Program

National and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine, Professor • University of Tsukuba, Professor (2)• University of Hyogo, Professor • Saitama University, Associate Professor • The University of Electro-Communications, Associate Professor• The University of Tokyo, Associate Professor • Kyushu University, Associate Professor • Kyushu Institute of Technology, Associate Professor• The Institute of Statistical Mathematics, Associate Professor • University of Tsukuba, Lecturer • Hokkaido University, Assistant• Chiba University, Assistant • Tokyo Institute of Technology, Assistant • Hiroshima University, Assistant• Kyushu University, Assistant Professor • University of the Ryukyus, Assistant • The Institute of Statistical Mathematics, Assistant Professor (5)• The University of Tokyo, Project Researcher • Tokyo Institute of Technology, Research Fellow• Nara Institute of Science and Technology, Research Fellow • The Institute of Statistical Mathematics, Project Researcher (7)• Bank of Japan, Project Post • Japan Broadcasting Corporation • Financial Services Agency, Financial Research and Training Center, Researcher• The Institute of Statistical Mathematics, JST CREST Research Fellow • JST Basic Research Programs Doctoral Research Fellow• Railway Technical Research Institute, Senior Researcher • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School, Teacher • The Institute of Statistical Mathematics, Research Fellow (2)Private universities• Sapporo Gakuin University, Professor • Meiji University, Professor • Doshisha University, Professor• Tokyo Health Care University, Associate Professor • Nihon University, Associate Professor • Tokyo University of Information Science, Lecturer• Josai University, Lecturer • Sapporo Gakuin University, Full-Time Lecturer • Tokyo Women’s Medical University, Postdoctoral Fellow
Foreign universities• Asia-Pacific Center for Security Studies Department, Associate Professor • Central South University, Professor• Hong Kong Baptist University, Lecturer • Jahangirnagar University, Professor • Jahangirnagar University, Associate Professor (2)• Massey University, Research Fellow • The University of Warwick, Research Fellow • University of Otaga, Research Fellow• University of Rajshahi, Associate Professor • University of South Carolina, Research Fellow • Victoria University, Senior Lecturer
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory, Research Fellow • NTT Communication Science Laboratories, Research Fellow• Seiwa Kikaku • Toyota Motor Corporation, Higashi-Fuji Technical Center, Research Fellow • NLI Research Institute, Chief Researcher• Sankyo Co., LTD. • Mizuho Trust and Banking, Senior Researcher (2) • JP Morgan Trust Bank Limited, Vice President (Hosei University, Part-Time Lecturer)• ATR Computational Neuroscience Laboratories, Research Fellow • Schlumberger Limited • Macquarie Securities, Japan, Quantitative Analyst• Non-Life Insurance Rating Organization of Japan, Staff Member • Open Technologies Corporation

19

Outreach Activities

Tutorial Courses
䡵 History
The statistical education program started in 1944, the year that the Institute of Statistical Mathematics
was founded, as an education program at the Numerical Computation Training Center of the Science
Research Technical Assistant Training Center of the Ministry of Education, located in the Institute. In
1947, the affiliated Statistical Technician Training Center was opened as an educational organization for
statistical technicians and instructors, in order to improve the levels of staff training within the statisticalorganizations of the government and to supply statisticians.
As social needs have changed, the purpose of the education program has gradually shifted away fromthe initial aim of supplying well qualified statistical technicians for the government, towards statisticaleducation for working people. Tutorial courses were therefore initiated. Later, statistical methods became
more widely used across a broad range of fields. Consequently, more extensive and sophisticated statisticaleducation was required. To meet this demand, tutorial courses were expanded to cover a wider range ofstatistical topics. From 1965 to 1985, six to eight courses were held annually, not only in Tokyo, but also inprovincial cities such as Osaka, Okayama and Fukuoka.
In 1985, the Institute was reorganized as a member of the Inter-University Research Institute Corporation,and the affiliated Statistical Technician Training Center was abolished. However, in response to consistentpublic demand for tutorial courses, the Center for Engineering and Technical Support, together withother departments, ran three to four courses annually. In 2005, the number of courses rose to 13. In 2008,
the number of courses was 13.

䡵 Courses
The total number of courses held from 1969 to March, 2009 was 246, with a total of 18,266 participants.
These courses covered a wide range of fields from basic to applied statistics. The following table lists thecourses held in the past 3 years:
Year

Category

2006

Standard course

A Course on Time Series Analysis for Economics and Finance

June

40

Standard course

Advances in Kernel Methods: SVM, Nonlinear Data Analysis, and Structured Data

July

73

Basic course

Basic Medical Statistics Using R

July

20

Basic course

Introduction to Statistics

July

69

Standard course

Lectures on Information Theory and Mobile Telecommunication Technologies— Systems and Hardwares for Large-Scale Data Processing —August-September

13

Basic course

International Standardization of Statistical Methods — Precision and Truenessof Measurement Methods and Results — Capability of Detection

September

22

2007

Outreach Activities
20

Title

Month

Number of participants

Advanced course

A New Trend of Adaptive and Learning Control Theory

September

14

Basic course

A Game Theoretic Approach to Mathematical FinanceNovember21

Basic course

Introduction to Quantitative Methods for Social Sciences

November-January

43Standard course

Statistical Pattern Recognition

November

65
13

Basic course

Introduction to Statistical Data Analysis

November-March

Standard course

Statistical Mathematics of Rock-Scissors-Paper Game

November-December 7Standard course

An Introduction to Statistical Analysis Based on the Theory of Martingales

December38

Basic course

Introduction to Risk Analysis with R— Application of Tree-based and Nonparametric Modelling —January

49

Basic course

Introduction to Survey Data Analysis Using R

February

40

Basic course

Introduction to Quantitative Methods for Social Sciences

May-July

54

Basic course

Introduction to Sampling Methods and Sample Surveys

June

50

Basic course

Elementary Course on Time Series Analysis

July

61

Basic course

Introduction to Statistics

July

70

Standard course

Statistics of Extremes

September

61

Basic course

Introduction to Statistical Data Analysis Focused on Multivariate Analysis

October47

Basic course

Quantification Methods for Qualitative Data

October-December 46

Year

2008

2009

Category

Title

Month

Basic course

Recent Topics on International Standardization of Statistical Methods— Standardization of Sampling Inspection and Statistical Process Management —NovemberNumber of participants

11

Basic course

Statistical Quality Control

November

16Standard course

Taguchi Methods for Robust Design

November

28Standard course

Geometrical Structures Underlying Information Quantities:
Mathematics of Kullback-Leibler Information

December52

Basic course

Statistical Causal Analysis by Structural Equation Modelling

February

58

Standard course

Regression Models for Count Data and Their Extension

February

63

Basic course

Data Analysis and Simulation with R

May

69

Basic course

The Grammar of Science and Descriptive Statistical Methods

June

47

Basic course

Introduction to Statistics

July

79

Standard course

Spatial Point Pattern Analysis — Introduction from Biological Examples

August46

Standard course

Statistics of Extremes

August48

Standard course

An Introduction to Statistical Analysis by the Theory of Martingales

September48

Basic course

Analysis of Sample Surveys with R

September

41

Basic course

Introduction to Multivariate Analysis

October

71

Standard course

Data Assimilation: State Space Model and Simulation

October72

Basic course

Quality Control, Quality Engineering (Taguchi Method)

November17

Basic course

Introduction to Pharmacoepidemiology

November

71Standard course

Understanding of Evolutionary Computation: Statistical Viewpoint

December

56

Standard course

Markov Chain Monte Carlo : Basics and Examples

February78

The schedule of tutorial courses can be found on the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

Statistical Mathematics Seminar
The Institute holds a one-hour seminar every Wednesday to showcase the latest studies of our academic
staff and guest researchers. The seminar is open to the public. For details, please visit the website of theInstitute.
http://www.ism.ac.jp/

Open Lecture

Outreach Activities

We hold an open lecture during Education and Culture Week every year (November 1 through 7), tointroduce the Institute’s activities and to promote statistical science. We invite lecturers to speak on atimely topic relating to statistical science. The lecture is open to the general public. For further information, please visit the website of the Institute of Statistical Mathematics.
http://www.ism.ac.jp/21Outreach Activities

Consultation about Statistical Science
The Institute provides a consultation service on statistical science for the general public and researchers
as a means of actively sharing the benefits of our research with society. This service operates throughout
the year. Please contact the Center for Engineering and Technical Support. The consultation service covers
a variety of topic ranging from the basics of statistics to more specialized issues. Half of the advice receivers
are from the private sector, and the rest are staff from public organizations, university teachers, andstudents. Each teacher at the Institute directly takes charge of about 20 specialized cases annually,
about 40% of which are publicized at academic conferences, as being of benefit to society.

Annual Research Meeting for 2008
The Annual Research Meeting of the Institute was held on March 18 and 19, 2009. This annual meeting is
an opportunity for the Institute’s researchers and visiting researchers to present the results of their studies
from the previous year. The meeting has been held every year since the foundation of the Institute in 1944.
In the early years, as there were few staff members and participants, they were able to have enthusiastic
discussions all day long. However, as the number of staff members has increased, the meeting is now held
over two days and each presenter is given 13 minutes to report on the results of his/her own research. A
wide variety of topics is covered, ranging from statistical contributions to issues facing today’s society tomore fundamental studies. This year’s meeting started with an opening address by Junji Nakano, andgreetings from Genshiro Kitagawa. A total of 48 research education staff and 6 visiting researchers then gave
their presentations. In addition, a poster session was held by 35 research fellows and graduate students
of Sokendai. From 2004, we have begun to compile and distribute proceedings in advance. We also host
visitors from other organizations. The program for this meeting is available from the website.
http://www.ism.ac.jp/

Outreach Activities
22

Finance and Buildings
Finance and Buildings

Administration Subsidy and Others ( 2008)
Type

Personnel expenses

Non-personnel expenses

Total

762,6811,080,970

1,843,651

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2008)
Type

Subcontracted
research

Joint researchContribution for
scholarship

Total

Items

6

3

2

11

Income84,624

13,65032,000130,274
Unit: ¥1,000

Grant-in-Aid for Scientific Research ( 2008)
Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Priority Areas

1

2,700

Grant-in-Aid for Scientific Research ( A )665,650Grant-in-Aid for Scientific Research ( B)525,610Grant-in-Aid for Scientific Research (C)1317,290Grant-in-Aid for Exploratory Research

2

1,600

Grant-in-Aid for Young Scientists ( A )1

13,390Grant-in-Aid for Young Scientists ( B)

8

8,190

Grant-in-Aid for Young Scientists (Start-up)

1

1,716

Grant-in-Aid for JSPS Fellows

1

1,100

38

137,246

Total

Unit: ¥1,000

Site and Buildings ( As of October 1, 2009)
Site Area

62,450m 2

Area for Buildings (total)

11,855m 2

23

Finance and Buildings

Introduction to the Tachikawa Campus
With three newly added strategic research centers, our campus in Hiroo has become increasingly
crowded in recent years. The Tachikawa new campus offers ample space for conducting joint researches
in a more extensive and flexible manner. Taking advantage of the large space available for joint researches
and facilities for visitors, we, under a new concept, would like to reinforce the functions of inter-university
research institute by helping develop Networks of Excellence (NOEs) and allowing for long-stay joint
researches as well as the traditional form of open-type joint researches, and contribute to academic andsocial development.

24

Front of Building

East Side of Building

Main Gate

Entrance Hall

Atrium Space

Library

Auditorium

Conference Room

Seminar Room

LoungeRefresh Corner

Courtyard

Finance and Buildings
Finance and Buildings

3

6F
14
3

5F
3

4F
3
9

13

11 12

10

3F
3
8

6

5

7

2F
1
3

2
4

1F
3
15

B1
The Institute of Statistical Mathematics

1 Main Entrance

6 Conference Room 1
911 Seminar Room 3

National Institute of Polar Research

2 Reception

10
7 Conference Room 2

12 Seminar Room 4

National Institute of Japanese Literature

3 Lift11
8 Seminar Room 1

13 Seminar Room 5

Common Area

4 Library

12
9 Seminar Room 2

14 Seminar Room 6

5 Auditorium

10
13 Lecture Hall

15 Computer Room

25

Facilities and Equipment

Computation Resources ( As of October 1, 2009)

Facilities and Equipment

Since January 2004, “Supercomputer System for Statistical Science” has been in operation and has analyzed
a large volume of statistical data. The main components of this system comprise a SGI Altix3700 Super Cluster(a parallel computer system with 256 Itanium2 processors and about 2 TB main memory), a NEC SX-6 (avector-type computer system with 12 vector processors and 128 GB main memory), and a HITACHI SR11000 (aparallel computer sub-system with 64 Power4+ processors and 128 GB main memory). In January 2006, “System
for Computational Statistics” was renewed. The main components of this system consist of a HP XC4000 Cluster
system with 256 Opteron processors for computing node and 640 GB main memory, a SGI Prism visualization
system with 16 Itanium2 processors and 32 GB main memory, and a large display system (Multi Opt View).
In December 1998, an Ethernet network using 1000Base-SX as a main trunk and 100Base-TX as branches waslaid out as a Local Area Network. Workstations, personal computers in researchers’ offices, “Supercomputer
System for Statistical Science”, and “System for Computational Statistics” were all connected to the network.
This Local Area Network enables distributed processing and computation resources and statistical data to
be used effectively. The development of programs, which run on “Supercomputer System for Statistical
Science” and on “System for Computational Statistics” by operating fromworkstations and personal computers in researchers’ offices, is also
underway. To encourage joint research with researchers both in Japanand abroad, as well as the exchange of e-mails, the network is connectedto the internet through SINET. The connection speed of 1.5 Mbps during
1999 has risen to 100 Mbps since July 2002. Since April 2007, thenetwork has also been connected to the SINET3 with 2.4 Gbps bandwidth. Some machines are able to communicate at a rate of 1 Gbps.
Through terminal servers, the network is also accessible from a publicline. In addition, comprehensive network security measures have been
implemented such as the adoption of anti-virus software as well as a
network monitoring system.
In October 2009, the Institute moved to a new building in Tachikawa,Tokyo. Present supercomputer systems will work until December 2009 in
the old building in Hiroo. New supercomputer systems will be introduced
in January 2010 in Tachikawa.
SGI Altix3700 Super Cluster, a parallel computer

Library and Materials ( As of April 1, 2009 )We have a large number of major Japanese/foreign journals covering a wide variety of fields including
statistics, mathematics, computer science and informatics. In addition, we also have a large libraryconsisting of books on humanities, social science, biology, medical science, science and engineering.
Besides contributed to Japanese and foreign publications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Statistical Mathematics (English; Springer), Proceedings of theInstitute of Statistical Mathematics (Japanese), Research Report (Statistical Researches mainly related to
the Japanese National Character), Computer Science Monographs, Cooperative Research Reports (forcollaborative research projects), Research
Memorandum, ISM Reports on Statistical
JapaneseJapanese
ForeignComputing, and ISM Report on Research andbooks
periodicals
periodicals
Education.
15,117All materials are properly catalogued and
1,210
980
27%
Books
Periodicals
55%
45%
can
be searched from the web in order to55,346
2,190
meet the needs of researchers working in awide of fields. We also accept photocopy
Foreign books
40,229requests.
73%

26

Organization

Organization Diagram ( As of April 1, 2009 )Cooperative
Research
Committee
Council
Scientific
AdvisoryBoard䡵 Spatial and Time Series Modeling Group䡵 Intelligent Information Processing Group䡵 Graph Modeling Group

Department of Data Science䡵 Survey Research Group䡵 Multidimensional Data Analysis Group䡵 Computational Statistics Group

Department of Mathematical
Analysis and Statistical Inference䡵 Mathematical Statistics Group䡵 Learning and Inference Group䡵 Computational Mathematics Group

Prediction and Knowledge Discovery
Research Center (PKDRC)

䡵 Molecular Evolution Research Group䡵 Data Assimilation Research Group䡵 Statistical Seismology Research Group䡵 Statistical Genome Diversity Research Group䡵 Food and Drug Safety Research Group䡵 Environmental Risk Research Group䡵 Financial Risk and Insurance Research Group䡵 Research Group for Reliability andQuality Assurance of Service and ProductRisk Analysis Research Center( RARC )Vice DirectorGeneral

Research Innovation Center䡵 Social Survey Information Research Group䡵 Functional Analytic Inference Research Group䡵 Advanced Monte Carlo Algorithm Research Group䡵 Random Number Research Group

Center for Engineering andTechnical Support

䡵 Computing Facilities Unit䡵 Information Resources Unit䡵 Media Development Unit

Library

GeneralAdministration
Section䡵 General Affairs Unit
䡵 Personnel Unit
䡵 Research Cooperation Unit

Accounting
Section䡵 General Affairs Unit
䡵 Auditing Unit䡵 Supplies Unit
䡵 Property Maintenance Unit

Administration
OfficeOrganization

DirectorGeneral

Department of Statistical ModelingIntellectual Property Unit
Administration Planningand Coordination Section

Information and Public Relation Unit
Evaluation Unit
Planning Unit

Number of Staff ( As of October 1, 2009 )Type

Director-General

Director- Professor Associate
GeneralProfessor

Assistant Administrative Technical
Professor
Staff
Staff

1

Department of Statistical Modeling

Total

1
6

6

5

17

Department of Data Science

8

6

5

19

Department of Mathematical Analysis and Statistical Inference

5

4

5

14
8 (1)

8 (1)

13

1

14

13

9 (1)

73 (1)

Center for Engineering and Technical Support
Administration Office
Total

1

19

16

15

(

) Staff of reemployment

27

Staff ( As of October 1, 2009)
Director-General

Genshiro KITAGAWA

Vice Director-General

Masaharu TANEMURA

Vice Director-General

Yoshiyasu TAMURA

Vice Director-General

Tomoyuki HIGUCHI

Department of Statistical Modeling
Director

䡵 Spatial and Time Series Modeling Group
Prof.
Prof.
Prof.

Masaharu TANEMURA
Yosihiko OGATA
Tomoyuki HIGUCHI

Makio ISHIGURO
Jiancang ZHUANG
Shinya NAKANOHai Yen SIEW
Eiki TANAKA

Yoshinori KAWASAKI
Kenichiro SHIMATANI
Genta UENO
Ryo YOSHIDA

Assist. Prof.

Assoc. Prof.

Yukito IBA
Yumi TAKIZAWA
Visiting Assoc. Prof.
Masataka GOTO

Assist. Prof.

Assoc. Prof.

Project Researcher

Hiroshi SOMEYAKen ISHIKAWA

Assist. Prof.

Wataru MATSUMOTO

Shigeyuki MATSUI
Satoshi YAMASHITA
Sumie UEDA

Assist. Prof.

Toshio OHNISHI
Toshihiko KAWAMURA

Kazunori YAMAGUCHI
Naomasa MARUYAMA
Koji KANEFUJI
Seisho SATO

Visiting Assoc. Prof. Norikazu IKOMA

Assoc. Prof.
Assoc. Prof.
Assoc. Prof.
Assist. Prof.

Assist. Prof.
Project Researcher
Project Researcher䡵 Intelligent Information Processing Group
Prof.

Organization

Prof.
Prof.

Makio ISHIGURO
Tomoko MATSUI
Kenji FUKUMIZU

䡵 Graph Modeling Group
Assoc. Prof.

Jun ADACHIAssist. Prof.

Ying CAO

Department of Data Science
Director䡵 Survey Research Group
Prof.
Prof.
Visiting Prof.

Takashi NAKAMURA
Ryozo YOSHINO
Ikuo NASU

Takashi NAKAMURA

Tadahiko MAEDA
Takahiro TSUCHIYA
Visiting Assoc. Prof.
Takahiro HOSHINO
Assoc. Prof.
Assoc. Prof.

䡵 Multidimensional Data Analysis Group
Prof.
Prof.
Prof.

Toshiharu FUJITA
Hiroe TSUBAKI
Nobuhisa KASHIWAGIProf.
Assoc. Prof.
Assist. Prof.

Assist. Prof.

䡵 Computational Statistics Group
Prof.
Prof.
Visiting Prof.
Visiting Prof.

Yoshiyasu TAMURA
Junji NAKANOMichiko WATANABE
Yuichi MORI

Visiting Prof.
Assoc. Prof.
Assoc. Prof.
Assoc. Prof.

Assist. Prof.

Nobuo SHIMIZU

Department of Mathematical Analysis and Statistical Inference
Director

䡵 Mathematical Statistics Group
Prof.
Assoc. Prof.

Satoshi KURIKI
Yoichi NISHIYAMA

Assist. Prof.
Assist. Prof.

Shinto EGUCHI

Takaaki SHIMURAKei KOBAYASHI

Assist. Prof.

Shiro IKEDA
Hironori FUJISAWA

Assist. Prof.
Assist. Prof.

Tadayoshi FUSHIKI
Masayuki HENMI

Atsushi YOSHIMOTO
Satoshi ITO

Project Researcher

Hayato TAKAHASHI

Project Researcher

Shogo KATO
Xiaoling DOU

䡵 Learning and Inference GroupProf.

Shinto EGUCHI

Assoc. Prof.
Assoc. Prof.

䡵 Computational Mathematics Group
Prof.
Prof.

28

Takashi TSUCHIYA
Yoshihiko MIYASATO

Prof.
Assoc. Prof.

Organization

Prediction and Knowledge Discovery Research Center (PKDRC)
Director䡵 Molecular Evolution Research Group
Assoc. Prof.

Jun ADACHI

Shinto EGUCHI

Assist. Prof.

Ying CAO

Adjunct Prof.

Masami HASEGAWA

Assist. Prof.

Ryo YOSHIDA
Shinya NAKANO

Project Researcher
Project Researcher

Tomoko IMOTO
Hiromichi NAGAO

Jiancang ZHUANG
Mitsuhiro MATSUURA

Project Researcher

Ushio TANAKA

Hironori FUJISAWA

Assist. Prof.

Tadayoshi FUSHIKI
Shogo KATO

䡵 Data Assimilation Research Group
Prof.
Assoc. Prof.

Tomoyuki HIGUCHI
Genta UENO

Assist. Prof.

Prof.
Visiting Prof.

Yosihiko OGATA
Shinji TODA

Assist. Prof.
Project Professor

Organization

䡵 Statistical Seismology Research Group䡵 Statistical Genome Diversity Research Group
Prof.
Prof.
Assoc. Prof.

Shinto EGUCHI
Satoshi KURIKI
Shiro IKEDA

Assoc. Prof.

Visiting Assoc. Prof. Kanta NAITO

Assist. Prof.

Risk Analysis Research Center (RARC)
Director
Vice Director / Coordinator

Hiroe TSUBAKI
Toshiharu FUJITA

Coordinator
Coordinator
Coordinator

Koji KANEFUJI
Yoshinori KAWASAKI
Toshihiko KAWAMURA䡵 Food and Drug Safety Research Group
Prof.
Prof.
Prof.

Toshiharu FUJITA
Hiroe TSUBAKI
Shigeyuki MATSUIVisiting Prof.

Manabu IWASAKI
Tosiya SATO
Visiting Prof.
Yoshimitsu HIEJIMA
Visiting Assoc. Prof. Satoshi AOKIAssist. Prof.

Visiting Prof.

Assist. Prof.

Project Researcher

Takaaki SHIMURA
Masayuki HENMI
Yosuke FUJII
Takehiko YAMAGUCHI

Hidetoshi KONNO
Kazuo YAMAMOTO
Yoshiro ONO
Hideshige TAKADA

Assoc. Prof.

Koji KANEFUJI

Project Researcher

䡵 Environmental Risk Research Group
Prof.
Prof.
Visiting Prof.
Visiting Prof.

Nobuhisa KASHIWAGI
Atsushi YOSHIMOTO
Yukio MATSUMOTO
Kunio SHIMIZU

Visiting Prof.
Visiting Prof.
Visiting Prof.

Visiting Assoc. Prof. Hirokazu TAKANASHI
Visiting Assoc. Prof. Tomohiro TASAKI
Project Researcher

Masayuki KAGEYAMA

Assoc. Prof.

Project Researcher

Assoc. Prof.

Project Researcher

Masakazu ANDO
Kentaro AKASHI
Yoko TANOKURA

Visiting Prof.

䡵 Financial Risk and Insurance Research Group
Visiting Prof.
Visiting Prof.
Assoc. Prof.
Assoc. Prof.

Naoto KUNITOMO
Hiroshi TSUDA
Satoshi YAMASHITA
Seisho SATO

Yoshinori KAWASAKI
Yoichi NISHIYAMA
Visiting Assoc. Prof. Toshinao YOSHIBA

Project Researcher䡵 Research Group for Reliability and Quality Assurance of Service and Product
Prof.
Visiting Prof.
Visiting Prof.

Hiroe TSUBAKI
Kakuro AMASAKA
Kosei IWASE

Visiting Prof.
Visiting Prof.
Visiting Prof.

Kazuo TATEBAYASHI
Sadaaki MIYAMOTO
Shusaku TSUMOTO

Visiting Prof.
Assist. Prof.
Project Researcher

Yoichi KATO
Toshihiko KAWAMURA
Natsuki SANO

29

StaffResearch Innovation Center
Director

䡵 Social Survey Information Research Group
Prof.
Prof.

Takashi NAKAMURA
Ryozo YOSHINO

Kenji FUKUMIZU
Tadahiko MAEDA
Takahiro TSUCHIYA

Assoc. Prof.
Assoc. Prof.

Assist. Prof.

Wataru MATSUMOTO

Assist. Prof.

Sumie UEDA

Project Researcher

Masayuki KUMON
Christopher Andrew ZAPART䡵 Functional Analytic Inference Research GroupProf.

Kenji FUKUMIZU

Kei KOBAYASHI

Assist. Prof.

䡵 Advanced Monte Carlo Algorithm Research Group
Assoc. Prof.

Yukito IBA

Visiting Assoc. Prof.

Makoto KIKUCHI
Koji FUKUSHIMA

Visiting Assoc. Prof.

Toru ONODERA

Visiting Prof.

Project Professor Arnaud DOUCET䡵 Random Number Research Group

Organization

Prof.

Yoshiyasu TAMURA

Project Researchers
Project Professor

Atsushi FUKASAWA

Project Researcher
Project Researcher

Masaki OKUDA
Takayuki FUJII

Project Researcher

Center for Engineering and Technical Support
Director
Vice Director

Junji NAKANO
Satoshi YAMASHITA

Adjunct Prof.
Deputy Manager
Senior Specialist

Head of Computing Facilitise Unit

Kazuhiro NAKAMURA

Yasumasa BABA
Yuriko WATANABE
Saeko TANAKA

Head of Media Development Unit (Concurrent )Yuriko WATANABE

Head of Information Resources Unit (Concurrent) Saeko TANAKA

Library
HeadJunji NAKANO

Administration Planning and Coordination Section
Head of Intellectual Property Unit
Head of Information and Public Relations Unit

Masaharu TANEMURA
Tomoyuki HIGUCHIHead of Evaluation Unit
Head of Planning Unit

Yoshiyasu TAMURA
Tomoyuki HIGUCHI

Administration Office
General Manager

䡵 General Administration Section
Manager of General Administration Section
Deputy Manager of General Administration Section
Specialist ( General Affairs)

Yoshiki HAMA
Toshiyuki INADA
Fumio SUTO

Masaru SHIMODA
Chief of General Affairs Unit (Concurrent)
Chief of Personnel Unit
Chief of Research Cooperation Unit

Fumio SUTO
Motoyoshi URANO
Akihiko NAKAMURA

䡵 Accounting Section
Manager of Accounting Section
Deputy Manager of Accounting Section
Chief of General Affairs Unit

30

Toyokichi KITAHARA
Hiroshi YAMAMOTO
Hiroaki ARAI

Chief of Auditing Unit (Concurrent)
Chief of Supplies Unit
Chief of Property Maintenance Unit

Hiroaki ARAI
Tatsuya TAKAGI
Hirotomo SHIMIZU

Organization

Council of The Institute of Statistical Mathematics ( As of April 1, 2009)
Masanori IYEYutaka KANO
Toshinari KAMAKURA
Naoto KUNITOMO
Koji KURIHARA
Yoshiharu SATO
Kunio SHIMIZU
Kazuo SEIYAMA
Makoto TAIJI

Professor, Graduate School of Engineering Science, Osaka University
Professor, Faculty of Science and Engineering, Chuo University
Professor, Graduate School of Economics, the University of Tokyo
Professor, Graduate School of Environmental Science, Okayama University
Professor Emeritus, Hokkaido University
Professor, Faculty of Science and Technology, Keio University
Professor, Graduate School of Humanities and Sociology, the University of Tokyo
Group Director, Computational Systems Biology Research Group,
Advanced Computational Sciences Department, Advanced Science Institute, RIKEN
Professor, the Institute of Scientific and Industrial Research, Osaka University
Professor ( Vice Director-general, ISM (General Affairs) )Professor ( Vice Director-general, ISM (Assessment) )Professor ( Vice Director-general, ISM (Research Planning) )Professor ( Director of Department of Statistical Modeling, ISM)
Professor ( Director of Department of Data Science, ISM)

Organization

Takashi WASHIO
Masaharu TANEMURA
Yoshiyasu TAMURA
Tomoyuki HIGUCHI
Makio ISHIGURO
Takashi NAKAMURA
Shinto EGUCHI
Junji NAKANO
Hiroe TSUBAKI
Yosihiko OGATA
Satoshi KURIKI
Takashi TSUCHIYA

Professor, Optical and Infrared Astronomy Division, National Astronomical Observatory of Japan,
National Institutes of Natural Sciences

Professor ( Director of Department of Mathematical Analysis and Statistical Inference, ISM)
Professor ( Director of Center for Engineering and Technical Support, ISM)
Professor ( Director of Risk Analysis Research Center, ISM)
Professor ( Department of Statistical Modeling, ISM)
Professor ( Department of Mathematical Analysis and Statistical Inference, ISM)
Professor ( Department of Mathematical Analysis and Statistical Inference, ISM)

Cooperative Research Committee ( As of June 1, 2009 )Akifumi OIKAWA
Hidetoshi KONNO
Toshimitsu HAMASAKI
Michiko WATANABE
Yuichi MORI
Makio ISHIGURO
Ryozo YOSHINO
Shinto EGUCHI
Nobuhisa KASHIWAGI

Professor and Presidential Aide, The Graduate University for Advanced Studies
Professor, Graduate School of System and Information Engineering, University of Tsukuba
Associate Professor, Graduate School of Medicine, University of Osaka
Professor, Faculty of Economics, Toyo University
Professor, Faculty of Informatics, Okayama University of Science
Professor, ( Department of Statistical Modeling, ISM)
Professor, ( Department of Data Science, ISM)
Professor, ( Department of Mathematical Analysis and Statistical Inference, ISM)
Professor, ( Department of Data Science, ISM)

Research Ethics Review Committee ( As of April 1, 2009 )Specialist on Epidemiology and Social ResearchSpecialist on Epidemiology and Social Research
Person in Citizens' Position
Specialist in the field of Ethics and Law
The Institute of Statistical Mathematics
The Institute of Statistical Mathematics
The Institute of Statistical Mathematics
The Institute of Statistical Mathematics

Michio UMINO
Yoshihiko MIURA
Noboru MIYAMOTO
Hitomi NAKAYAMA
Toshiharu FUJITA
Takashi NAKAMURA
Tomoko MATSUI
Shinto EGUCHI

Professor Extraordinary, Tohoku University
Professor, School of Health and Social Service, Saitama Prefectural University
Principal, Koryo Junior High School, Minato-Ku
Lawyer, Kasumigaseki-Sogo Law Offices
Professor, Department of Data Science, ISM
Professor, Department of Data Science, ISM
Professor, Department of Statistical Modeling, ISM
Professor, Department of Mathematical Analysis and Statistical Inference, ISM

Professor Emeritus ( As of October 1, 2009)
Kameo MATSUSHITA
Shigeki NISHIHIRA
Tatsuzo SUZUKI
Giichiro SUZUKI

Ryoichi SHIMIZU
Noboru OHSUMI
Masakatsu MURAKAMI
Kunio TANABE

Tadashi MATSUNAWA
Masami HASEGAWA
Yoshiyuki SAKAMOTO
Takemi YANAGIMOTO

Yoshiaki ITOH
Yasumasa BABA
Katsuomi HIRANO

31

History

June, 1944

䢇

Based on a proposal submitted at an academic study conference in December 1943, the organization
was founded as an institute under the direct control of the Ministry of Education. This proposal aimed toprovide supervision for studies looking into the mathematical principles of probability and theirapplication, and was also intended to facilitate, unify and promote the publication of research results.

April, 1947

䢇The affiliated statistical specialists’ school was opened.

May, 1947

䢇The Institute was divided into the 1st Research Dept. (fundamental theories), the 2nd Research Dept.
(statistical theories for the natural sciences), and the 3rd Research Dept. (statistical theories for the social
sciences).

June, 1949䢇The Institute was placed under the control of the Ministry of Education because of the enforcement of
the Ministry of Education Establishment Law.

September, 1955 䢇

Reorganized into the 1st Research Dept. (fundamental theories), the 2nd Research Dept. (natural andsocial science theories), and the 3rd Research Dept. (operations, research, statistical analysis theories).
The laboratory system, comprising 9 laboratories and the research guidance promotion room, wasadopted.

October, 1969

䢇A new office building was constructed.

April, 1971

䢇The 4th Research Dept. (informatics theories) was instituted.

April, 1973

䢇The 5th Research Dept. (prediction and control theories) was instituted.

October, 1975

䢇The 6th Research Dept. (statistical theories of human behavior) was instituted.

November, 1979 䢇History

April, 1985

䢇

Reorganized as an Inter-University Research Institute owing to the revision of the Order for the Enforcement of the National school Establishment Law. The revised law required that the Institute would, as
an National Inter-University Research Institute, 1) conduct studies on statistical mathematics and its
application, 2) provide opportunities for university teachers or other researchers majoring in this field
to utilize the facility, and 3) contribute to the development of academic studies in universities.
At the same time, the 6 research departments were reorganized into 4 research departments(Fundamental Statistical Theory, Statistical Methodology, Prediction and Control, and Interdisciplinary
Statistics). The Statistical Data Analysis Center and the Statistical Education and Information Center
were instituted, and the affiliated Statistical Technician Training Center was abolished.

October, 1988

䢇The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science,part of the Graduate University for Advanced Studies ( SOKENDAI ).

June, 1999

䢇The Institute was reorganized as an Inter-University Research Institute based on the National School
Establishment Law.

April, 1993

䢇The Planning Coordination Chief System was instituted.

April, 1997䢇The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of
Statistical Computing, and the Statistical Education and Information Center was reorganized into theCenter for Information on Statistical Sciences.

September, 2003 䢇

32

The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted in the affiliated facility.

April, 2004

䢇The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research
Organization of Information and Systems of the Inter-University Research Institute based on the
National University Corporation Law. The Planning Coordination Chief System was abolished and theposition of Vice Director-General was instituted instead. The Dept. of Statistical Science in the Schoolof Mathematical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical
Science and the School of Multidisciplinary Sciences were instituted.

April, 2005

䢇The research organization was reorganized into three research departments (the Department of Statistical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical
Inference). The affiliated Center for Development of Statistical Computing, the Center for Information on
Statistical Sciences, and the Engineering and Technical Services Section were integrated into the Center
for Engineering and Technical Support. The affiliated facilities were reorganized as research departments,
and the Risk Analysis Research Center was instituted.

April, 2008

䢇The Research Innovation Center was instituted in the affiliated facility.
The Intellectual Property Unit was instituted.

October, 2009䢇The ISM was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

The Instituteof Statistical
Mathematics

Bus stop(Saibansho-mae)

to Kamikitadai

Judicial Courts

The National
Institute for Japanese Language

Takamatsu

Local Autonomy
College
Tachikawa
Police Station

Disaster
Medical Center

Tama
MonorailShowa Kinen Park

Tachikawa-kita

Ome Line

Chuo Line

Tachikawa to Shinjuku, Tokyo
Access to the ISM

Tama Monorailca. 7 min walk from Takamatsu Sta.
Tachikawa Bus
ca. 3 min walk from Saibansho-mae bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302 (General Affairs)

http://www.ism.ac.jp/